ArticleJournal of neurophysiology2016
Megamap: flexible representation of a large space embedded with nonspatial information by a hippocampal attractor network.
Article in Journal of neurophysiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- State transitions in the statistically stable place cell population correspond to rate of perceptual change.Current biology : CB · 2022Article
- Experience-dependent contextual codes in the hippocampus.Nature neuroscience · 2021Article
- Cognitive swarming in complex environments with attractor dynamics and oscillatory computing.Biological cybernetics · 2020Article
- Article
- Analysis of an Attractor Neural Network's Response to Conflicting External Inputs.Journal of mathematical neuroscience · 2018Article
- Neurons in Primate Entorhinal Cortex Represent Gaze Position in Multiple Spatial Reference Frames.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2018Article
- Memory System Neurons Represent Gaze Position and the Visual World.Journal of experimental neuroscience · 2018Article
- An Energy Model of Place Cell Network in Three Dimensional Space.Frontiers in neuroscience · 2018Article
- Theta-paced flickering between place-cell maps in the hippocampus: A model based on short-term synaptic plasticity.Hippocampus · 2017Article
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2 authors.
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Abstract
The problem of how the hippocampus encodes both spatial and nonspatial information at the cellular network level remains largely unresolved. Spatial memory is widely modeled through the theoretical framework of attractor networks, but standard computational models can only represent spaces that are much smaller than the natural habitat of an animal. We propose that hippocampal networks are built on a basic unit called a "megamap," or a cognitive attractor map in which place cells are flexibly recombined to represent a large space. Its inherent flexibility gives the megamap a huge representational capacity and enables the hippocampus to simultaneously represent multiple learned memories and naturally carry nonspatial information at no additional cost. On the other hand, the megamap is dynamically stable, because the underlying network of place cells robustly encodes any location in a large environment given a weak or incomplete input signal from the upstream entorhinal cortex. Our results suggest a general computational strategy by which a hippocampal network enjoys the stability of attractor dynamics without sacrificing the flexibility needed to represent a complex, changing world.
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